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Tree-structured model diagnostics for linear regression
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DOI：10.1007/s10994-008-5080-8.png)
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Abstract
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This paper studies model diagnostics for linear regression models. We propose two tree-based procedures to check the adequacy of linear functional form and the appropriateness of homoscedasticity, respectively. The proposed tree methods not only facilitate a natural assessment of the linear model, but also automatically provide clues for amending deficiencies. We explore and illustrate their uses via both Monte Carlo studies and real data examples.
Keywords:
AIC
CART
Heteroscedasticity
Linear models
Regression trees
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